Reconnaissance d'entités nommées

Résumé

La reconnaissance d'entités nommées est une sous-tâche de l'activité d'extraction d'information dans des corpus documentaires. Elle consiste à rechercher des objets textuels (c'est-à-dire un mot, ou un groupe de mots) catégorisables dans des classes telles que noms de personnes, noms d'organisations ou d'entreprises, noms de lieux, quantités, distances, valeurs, dates, etc. Principe À titre d'exemple, on pourrait donner le texte qui suit, étiqueté par un système de reconnaissance d'entités nommées utilisé lors de la campagne d'évaluation MUC: :Henri a acheté 300 actions de la société AMD en 2006 :Henri a acheté 300 actions de la société AMD en 2006. Le texte de cet exemple est étiqueté avec des balises XML, respectant le standard d'étiquetage ENAMEX. La plupart des systèmes d'étiquetages utilisent des grammaires formelles associées à des modèles statistiques, éventuellement complétées par des bases de données (listes de prénoms, de noms de villes ou de pays par exem

Source officielle

https://fr.wikipedia.org/wiki/Reconnaissance_d'entit%C3%A9s_nomm%C3%A9es

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Differentiable Patch Selection for Image Recognition

Jean-Baptiste Francis Marie Juliette Cordonnier, Aravindh Mahendran

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high resolution images. Our method may be interfaced with any downstream neural network, is able to aggregate information from different patches in a flexible way, and allows the whole model to be trained end-to-end using backpropagation. We show results for traffic sign recognition, inter-patch relationship reasoning, and fine-grained recognition without using object/part bounding box annotations during training.

IEEE COMPUTER SOC2021

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We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. HIPE-2022 is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of the evaluation lab is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets.

Springer International Publishing2022

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Named Entity Processing for Historical Texts

Maud Ehrmann, Matteo Romanello

Recognition and identification of real-world entities is at the core of virtually any text mining application. As a matter of fact, referential units such as names of persons, locations and organizations underlie the semantics of texts and guide their interpretation. Around since the seminal Message Understanding Conference (MUC) evaluation cycle in the 1990s, named entity-related tasks have undergone major evolutions until now, from entity recognition and classification to entity disambiguation and linking. Recently, NE processing has been called upon to contribute to the domain of digital humanities, where massive digitization of historical documents is producing huge amounts of texts. De facto, NE processing tools are increasingly being used in the context of historical documents. Research activities in this domain target texts of different nature (e.g., publications by cultural institutions, state-related documents, genealogical data, historical newspapers) and different tasks (NE recognition and classification, entity linking, or both). Experiments involve different time periods (from 16th to 20th c.), focus on different domains, and use different typologies. This great variety demonstrates how many and varied the needs – and the challenges – are, but makes performance comparison difﬁcult, not to say impossible. The objective of this tutorial is to provide the participants with essential knowledge with respect to a) NE processing in general and in DH, and b) how to apply NE recognition approaches.

2019

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